Personalized Machine Learning (Hardcover, New Ed)

Every day we interact with machine learning systems offering individualized predictions for our entertainment, social connections, purchases, or health. These involve several modalities of data, from sequences of clicks to text, images, and social interactions. This book introduces common principles and methods that underpin the design of personalized predictive models for a variety of settings and modalities. The book begins by revising 'traditional' machine learning models, focusing on adapting them to settings involving user data, then presents techniques based on advanced principles such as matrix factorization, deep learning, and generative modeling, and concludes with a detailed study of the consequences and risks of deploying personalized predictive systems. A series of case studies in domains ranging from e-commerce to health plus hands-on projects and code examples will give readers understanding and experience with large-scale real-world datasets and the ability to design models and systems for a wide range of applications.

General

Imprint:	Cambridge UniversityPress
Country of origin:	United Kingdom
Release date:	February 2022
Authors:	Julian McAuley
Dimensions:	235 x 159 x 21mm (L x W x T)
Format:	Hardcover
Pages:	350
Edition:	New Ed
ISBN-13:	978-1-316-51890-8
Categories:	Books > Business & Economics > Business & management > Management of specific areas > General Books > Computing & IT > Applications of computing > Databases > General Books > Computing & IT > Applications of computing > Artificial intelligence > Machine learning Books > Computing & IT > Applications of computing > Artificial intelligence > Natural language & machine translation Promotions
LSN:	1-316-51890-6
Barcode:	9781316518908

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